{
 "cells": [
  {
   "cell_type": "code",
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   "id": "ef3d3796",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'torch'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mRNN\u001b[39;00m(nn\u001b[38;5;241m.\u001b[39mModule):\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "class RNN(nn.Module):\n",
    "   def __init__(self, word_count, embedding_size, hidden_size, output_size):\n",
    "    super(RNN, self).__init__()\n",
    "    self.hidden_size = hidden_size\n",
    "    self.embeding=torch.nn.Embedding(word_count,embedding_size)\n",
    "    self.rnn=nn.RNN(embedding_size)\n"
   ]
  }
 ],
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